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https://github.com/annahedstroem/gef

Code and notebooks to paper "Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions" (TMLR, 2025)

https://github.com/annahedstroem/gef

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Repository

Code and notebooks to paper "Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions" (TMLR, 2025)

Basic Info
  • Host: GitHub
  • Owner: annahedstroem
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 19.2 MB
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Created almost 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md



Task-agnostic Interpretability Evaluator

PyTorch



This repository contains the code and experiments for the paper "Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions" by Hedström et al., 2025 (with Survey Certification!).

Getting started! <!--Python version--> <!--Code style: black--> <!--PyPI version--> <!--Python package--> <!--Launch Tutorials-->

Please note that this repository is under active development!

Citation

If you find this work interesting or useful in your research, use the following Bibtex annotation to cite us:

bibtex @article{hedstrom2025explanation, title={Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions}, author={ Hedstr{\"o}m, Anna and Bommer, Philine Lou and Tom, Burns and Lapuschkin, Sebastian and Samek, Wojciech and H{\"o}hne, Marina M-C }, journal={Transactions on Machine Learning Research}, year={2025}, url={https://openreview.net/forum?id=ukLxqA8zXj}, }

Repository overview

The repository is organised as follows: - The src/ folder contains all necessary functions. - The nbs/ folder includes notebooks for generating the plots in the paper and for benchmarking experiments. - The assets/ folder contains all files to reproduce the experiments. - The tests/ folder contains the tests.

All evaluation metrics used in these experiments are implemented in Quantus, a widely-used toolkit for metric-based XAI evaluation. Benchmarking is performed with tools from MetaQuantus, a specialised framework for meta-evaluating metrics in interpretability.

Installation

Install the necessary packages using the provided requirements.txt:

bash pip install -r requirements.txt

Package requirements

Required packages are:

setup python>=3.10.1 torch>=2.0.0 quantus>=0.5.0 metaquantus>=0.0.5 captum>=0.6.0

Thank you

We hope our repository is beneficial to your work and research. If you have any feedback, questions, or ideas, please feel free to raise an issue in this repository. Alternatively, you can reach out to us directly via email for more in-depth discussions or suggestions.

📧 Contact us: - Anna Hedström: hedstroem.anna@gmail.com

Thank you for your interest and support!

Owner

  • Name: Anna Hedström
  • Login: annahedstroem
  • Kind: user
  • Location: Berlin, Germany

ML PhD student @TU-Berlin

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Dependencies

requirements.txt pypi
  • captum ==0.7.0
  • datasets ==2.17.1
  • horama *
  • matplotlib ==3.8.3
  • medmnist *
  • nltk *
  • numpy ==1.26.4
  • pandas ==2.2.1
  • scipy ==1.12.0
  • shap *
  • torch ==2.2.1
  • transformers ==4.38.1
  • zennit ==0.5.1